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- B. Hasırcıoǧlu, J. Gómez-Vilardebó and D. Gündüz, Bivariate Polynomial Codes for Secure Distributed Matrix Multiplication, IEEE Journal on Selected Areas in Communications, vol. 40, no. 3, pp. 955-967, March 2022.
- M. Chen, D. Gündüz, K. Huang, W. Saad, M. Bennis, A. V. Feljan, and H. V. Poor, Distributed Learning in Wireless Networks: Recent Progress and Future Challenges, IEEE Journal on Selected Areas in Communications, vol. 39, no. 12, pp. 3579-3605, Dec. 2021.
- E. Ozfatura, S. Ulukus and D. Gündüz, Coded Distributed Computing With Partial Recovery, IEEE Transactions on Information Theory, vol. 68, no. 3, pp. 1945-1959, March 2022.
- N. Mital, K. Kralevska, C. Ling and D. Gündüz, Functional Broadcast Repair of Multiple Partial Failures in Wireless Distributed Storage Systems, in IEEE Journal on Selected Areas in Information Theory, vol. 2, no. 4, pp. 1093-1107, Dec. 2021.
- D. B. Kurka and D. Gündüz, Bandwidth-Agile Image Transmission With Deep Joint Source-Channel Coding, in IEEE Transactions on Wireless Communications, vol. 20, no. 12, pp. 8081-8095, Dec. 2021.
- M. Boloursaz Mashhadi, M. Jankowski, T.-Y. Tung, S. Kobus, and D. Gündüz, Federated mmWave beam selection utilizing LIDAR data, IEEE Wireless Communications Letters, 2021.
- Y. Shao, D. Gündüz, and S. C. Liew, Federated edge learning with misaligned over-the-air computation, IEEE Transactions on Wireless Communications, to appear.
- B. Hasırcıoğlu, J. Gómez-Vilardebó and D. Gündüz, Bivariate Polynomial Coding for Efficient Distributed Matrix Multiplication, in IEEE Journal on Selected Areas in Information Theory, vol. 2, no. 3, pp. 814-829, Sept. 2021.
- M. M. Amiri, D. Gündüz, S. R. Kulkarni, and H. V. Poor, Convergence of federated learning over a noisy downlink, IEEE Transactions on Wireless Communications, vol. 20, no. 6, pp. 3643-3658, Jun. 2021.
- M. M. Amiri, D. Gunduz, S. Kulkarni, and H. V. Poor, Convergence of update aware device scheduling for federated learning at the wireless edge, IEEE Transactions on Wireless Communications, vol. 20, no. 6, pp. 3643-3658, Jun. 2021.
- T.-Y. Tung, J. Roig Pujol, S. Kobus, and D. Gündüz, Effective communications: A joint learning and communication framework for multi-agent reinforcement learning over noisy channels, IEEE Journal on Selected Areas in Communications, Vol. 39, No. 8, Aug. 2021.
- M. Mohammadi Amiri, T. M. Duman, D. Gündüz, S. R. Kulkarni, and H. V. Poor, Blind federated edge learning, IEEE Transactions on Wireless Communications, vol. 20, no. 8, pp. 5129-5143, Aug. 2021.
- M. Malekzadeh, B. Hasircioglu, N. Mital, K. Katarya, M. E. Ozfatura, and D. Gündüz, Dopamine: Differentially private federated learning on medical data, Second AAAI Workshop on Privacy-Preserving Artificial Intelligence (PPAI-21), 2021.
- E. Ozfatura, K. Ozfatura, and D. Gündüz, Time-Correlated Sparsification for Communication-Efficient Federated Learning, IEEE Int’l Symposium on Information Theory (ISIT), July 2021, pp. 461-466.
- E. Ceran, D. Gunduz, and A. Gyorgy, A reinforcement learning approach to age of information in multi-user networks with HARQ, in IEEE Journal on Selected Areas in Communications, vol. 39, no. 5, pp. 1412-1426, May 2021.
- M. Malekzadeh, A. Borovykh, and D. Gündüz, Honest-but-curious nets: Sensitive attributes of private inputs can be secretly coded into the classifier’s outputs, ACM SIGSAC Conference on Computer and Communications Security (CCS ’21), Nov. 2021.
- E. Ozfatura, K. Ozfatura, and D. Gündüz, FedADC: Accelerated Federated Learning with Drift Control, IEEE Int’l Symposium on Information Theory (ISIT), July 2021, pp. 467-472.
- O. Aygun, M. Kazemi, D. Gunduz and T. M. Duman, Hierarchical Over-the-Air Federated Edge Learning, IEEE Int’l Conference on Communications (ICC), 2021.
- S. F. Yilmaz, B. Hasırcıoğlu and D. Gündüz, “Over-the-Air Ensemble Inference with Model Privacy,” 2022 IEEE International Symposium on Information Theory (ISIT), Espoo, Finland, 2022, pp. 1265-1270
- N. Mital and D. Gündüz, “Bandwidth Expansion for Over-the-Air Computation with One-Sided CSI,” 2022 IEEE International Symposium on Information Theory (ISIT), Espoo, Finland, 2022, pp. 1271-1276.
- Q. Lan, Q. Zeng, P. Popovski, D. Gündüz and K. Huang, “Progressive Feature Transmission for Split Classification at the Wireless Edge,” in IEEE Transactions on Wireless Communications, 2022.
- R. Saha, M. Yemini, E. Ozfatura, D. Gunduz and A. Goldsmith, ColRel: Collaborative Relaying for Federated Learning over Intermittently Connected Networks, International Workshop on Federated Learning: Recent Advances and New Challenges in Conjunction with NeurIPS 2022 (FL-NeurIPS’22),Dec. 2022.
- F. Pase, D. Gündüz and M. Zorzi, “Rate-Constrained Remote Contextual Bandits,” in IEEE Journal on Selected Areas in Information Theory.
- T. -Y. Tung, D. B. Kurka, M. Jankowski and D. Gündüz, “DeepJSCC-Q: Constellation Constrained Deep Joint Source-Channel Coding,” in IEEE Journal on Selected Areas in Information Theory, to appear.
- D. Gündüz et al., “Beyond Transmitting Bits: Context, Semantics, and Task-Oriented Communications,” in IEEE Journal on Selected Areas in Communications, vol. 41, no. 1, pp. 5-41, Jan. 2023.
- B. Buyukates, E. Ozfatura, S. Ulukus and D. Gündüz, “Gradient Coding with Dynamic Clustering for Straggler-Tolerant Distributed Learning,” in IEEE Transactions on Communications, to appear.
- E. Ozfatura and D. Gündüz, “Uncoded Caching and Cross-Level Coded Delivery for Non-Uniform File Popularity,” in IEEE Transactions on Information Theory, vol. 68, no. 10, pp. 6842-6859, Oct. 2022.
- Z. Yang, J. -Y. Xia, J. Luo, S. Zhang and D. Gündüz, “A Learning-Aided Flexible Gradient Descent Approach to MISO Beamforming,” in IEEE Wireless Communications Letters, vol. 11, no. 9, pp. 1895-1899, Sept. 2022.
- S. U. Haque, S. Chandak, F. Chiariotti, D. Günduz and P. Popovski, “Learning to Speak on Behalf of a Group: Medium Access Control for Sending a Shared Message,” in IEEE Communications Letters, vol. 26, no. 8, pp. 1843-1847, Aug. 2022.
- T. Alshammari, S. Samarakoon, A. ElGabli, M. Bennis, “BayGo: Joint Bayesian Learning and Information-Aware Graph Optimization” in proc. IEEE ICC 21.
- D. Wen, M. Bennis, K. Huang, “Joint Parameter-and-Bandwidth Allocation for Improving the Efficiency of Partitioned Edge Learning,” IEEE Trans. Wireless Commun. 2020.
- A. Elgabli, J. Park, C. Ben Issaid, M. Bennis, “Harnessing wireless channels for scalable and privacy-preserving federated learning,” IEEE TCOM, accepted.
- C. Ben Issaid , Elgabli, J. Par , M. Bennis, “Communication Efficient Distributed Learning with Censored, Quantized, and Generalized Group ADMM,” IEEE TCOM 2021.
- Elgabli, J. Park, A. S. Bedi, C. Ben Issaid, M. Bennis, V. Aggarwal, ”Q-GADMM: Quantized Group ADMM for Communication Efficient Decentralized Machine Learning,” IEEE TCOM 2020.
- T. Alshammari, S. Samarakoon, A. ElGabli, M. Bennis, “Bayesian Learning and Information-Aware Graph Optimization” IEEE TCOM 2022 (submitted).
- M. Krouka, A. ElGabli, C. BenIssaid and M. Bennis, “Communication-Efficient Federated Learning: a Second Order Newton-type Method with Over-the-Air Aggregation” IEEE TGCN 2022.
- H. Seo, J. Park, M. Bennis, M. Debbah, “Semantics-Native Communication with Contextual Reasoning” IEEE TCOM 2022, under review.
- CB Issaid, A Elgabli, M Bennis, DR-DSGD: A Distributionally Robust Decentralized Learning Algorithm over Graphs, Transactions on Machine Learning Research, 2022.
- A Elgabli, CB Issaid, AS Bedi, K Rajawat, M Bennis, V Aggarwal, , “FedNew: A Communication-Efficient and Privacy-Preserving Newton-Type Method for Federated Learning,” International Conference on Machine Learning, 2022
- A. M. Elbir and S. Coleri, “Federated Learning for Hybrid Beamforming in mm-Wave Massive MIMO“, IEEE Communications Letters, vol. 24, no. 12, pp. 2795-2799, Dec. 2020.
- A. M. Elbir, S. Coleri and K. V. Mishra, “Federated Dropout Learning for Hybrid Beamforming With Spatial Path Index Modulation In Multi-User mmWave-MIMO Systems“, 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Toronto, Canada, June 2021.
- Elbir, A. M., & Coleri, S.. Federated Learning for Channel Estimation in Conventional and IRS-Assisted Massive MIMO. IEEE Transactions on Wireless Communications, vol. 21, no. 6, pp. 4255-4268, June 2022.
- A. M. Elbir, S. Coleri, “A Hybrid Architecture for Federated and Centralized Learning“, IEEE Transactions on Cognitive Communications and Networking, vol. 8, no. 3, pp. 1529-1542, September 2022.
- ] A. M. Elbir, B. Soner, S. Coleri, D. Gunduz and M. Bennis (2022). Federated Learning in Vehicular Networks. IEEE MeditCom, September 2022.
- A. M. Elbir, S. Coleri and K. V. Mishra, “Hybrid Federated and Centralized Learning”, EUSIPCO, August 2021.
- B. Turan and S. Coleri, “Machine Learning Based Channel Modeling for Vehicular Visible Light Communication”, IEEE Transactions on Vehicular Technology, vol. 70, no. 10, pp. 9659-9672, October 2021.
- B. Turan, A. Uyrus, O. N. Koc, E. Kar and S. Coleri, “Machine Learning Aided Path Loss Estimator and Jammer Detector for Heterogeneous Vehicular Networks”, IEEE GLOBECOM, December 2021.
- B. Soner and S. Coleri, “Visible Light Communication based Vehicle Localization for Collision Avoidance and Platooning”, IEEE Transactions on Vehicular Technology, vol. 70, no. 3, pp. 2167-2180, March 2021.
- G. Gurbilek, M. Koca and S. Coleri, “Blind Channel Estimation for DCO-OFDM based Vehicular Visible Light Communication”, Physical Communication, vol. 56, February 2023.
- B. Turan, O. Narmanlioglu, O. N. Koc, E. Kar, S. Coleri and M. Uysal, “Measurement based Non-Line-of-Sight Vehicular Visible Light Communication Channel Characterization”, IEEE Transactions on Vehicular Technology, vol. 71, no. 6, pp. 10110-10114, September 2022.
- B. Turan, O. N. Koc, E. Kar and S. Coleri, “Vehicular Visible LightCommunications Noise Analysis and Autoencoder Based Denoising”, EUCNC/6G Summit 2022.
- A. M. Elbir, G. Gurbilek, B. Soner, A. K. Papazafeiropoulos, P. Kourtessis and S. Coleri, “Vehicular Networks for Combating a Worldwide Pandemic: Preventing the Spread of COVID-19”, Smart Health, vol. 26, December 2022.
- F. E. Gumec, A. Reyhanoglu, Y. S. C. Kara, E. Kar, B. Turan, S. Coleri, M. Bennis, A. Elgabli, D. Gunduz, “Federated Learning for Pedestrian Detection in Vehicular Networks”, IEEE BlackSeaComm.